TY - GEN
T1 - Introducing children to machine learning concepts through hands-on experience
AU - Hitron, Tom
AU - Erel, Hadas
AU - Wald, Iddo
AU - Zuckerman, Oren
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/6/19
Y1 - 2018/6/19
N2 - Machine Learning (ML) processes are integrated into devices and services that affect many aspects of daily life. As a result, basic understanding of ML concepts becomes essential for people of all ages, including children. We studied if 10-12 years old children can understand basic ML concepts through direct experience with a digital stick-like device, in a WoZ-based experiment. To assess children's understanding we applied an experimental design including a pretest, a gesture recognition training activity, and a posttest. The tests included validating children's understanding of the gesture training activity, other gesture detection processes, and application to ML processes in daily scenarios. Our findings suggest that children are able to understand basic ML concepts, and can even apply them to a new context. We conclude that ML learning activities should allow children to sample their own examples and evaluate them in an iterative way, and proper feedback should be designed to gradually scaffold understanding.
AB - Machine Learning (ML) processes are integrated into devices and services that affect many aspects of daily life. As a result, basic understanding of ML concepts becomes essential for people of all ages, including children. We studied if 10-12 years old children can understand basic ML concepts through direct experience with a digital stick-like device, in a WoZ-based experiment. To assess children's understanding we applied an experimental design including a pretest, a gesture recognition training activity, and a posttest. The tests included validating children's understanding of the gesture training activity, other gesture detection processes, and application to ML processes in daily scenarios. Our findings suggest that children are able to understand basic ML concepts, and can even apply them to a new context. We conclude that ML learning activities should allow children to sample their own examples and evaluate them in an iterative way, and proper feedback should be designed to gradually scaffold understanding.
KW - Children
KW - Machine learning
KW - Physical experience
UR - https://www.scopus.com/pages/publications/85051493218
U2 - 10.1145/3202185.3210776
DO - 10.1145/3202185.3210776
M3 - Conference contribution
AN - SCOPUS:85051493218
T3 - IDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children
SP - 563
EP - 568
BT - IDC 2018 - Proceedings of the 2018 ACM Conference on Interaction Design and Children
PB - Association for Computing Machinery, Inc
T2 - 17th ACM Conference on Interaction Design and Children, IDC 2018
Y2 - 19 June 2018 through 22 June 2018
ER -